<<<<<<< Updated upstream Pandas Profiling Report

Overview

Dataset statistics

Number of variables28
Number of observations7406
Missing cells7405
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory224.0 B

Variable types

Categorical11
DateTime1
TimeSeries12
Numeric4

Alerts

State Code has constant value "4" Constant
County Code has constant value "19" Constant
Site Num has constant value "1028" Constant
Address has constant value "400 W RIVER ROAD" Constant
State has constant value "Arizona" Constant
County has constant value "Pima" Constant
City has constant value "Tucson" Constant
NO2 Units has constant value "Parts per billion" Constant
O3 Units has constant value "Parts per million" Constant
SO2 Units has constant value "Parts per billion" Constant
CO Units has constant value "Parts per million" Constant
SO2 1st Max Hour is highly correlated with SO2 1st Max ValueHigh correlation
SO2 AQI is highly correlated with O3 1st Max Value and 2 other fieldsHigh correlation
CO 1st Max Value is highly correlated with NO2 AQI and 3 other fieldsHigh correlation
CO 1st Max Hour is highly correlated with NO2 1st Max HourHigh correlation
State Code is highly correlated with Site Num and 9 other fieldsHigh correlation
County Code is highly correlated with State Code and 9 other fieldsHigh correlation
Site Num is highly correlated with State Code and 9 other fieldsHigh correlation
Address is highly correlated with State Code and 9 other fieldsHigh correlation
State is highly correlated with State Code and 9 other fieldsHigh correlation
County is highly correlated with State Code and 9 other fieldsHigh correlation
City is highly correlated with State Code and 9 other fieldsHigh correlation
NO2 Units is highly correlated with State Code and 9 other fieldsHigh correlation
O3 Units is highly correlated with State Code and 9 other fieldsHigh correlation
SO2 Units is highly correlated with State Code and 9 other fieldsHigh correlation
CO Units is highly correlated with State Code and 9 other fieldsHigh correlation
NO2 Mean is highly correlated with NO2 1st Max Value and 5 other fieldsHigh correlation
NO2 1st Max Value is highly correlated with NO2 Mean and 3 other fieldsHigh correlation
NO2 AQI is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
O3 Mean is highly correlated with NO2 Mean and 4 other fieldsHigh correlation
O3 1st Max Value is highly correlated with NO2 1st Max Hour and 7 other fieldsHigh correlation
O3 AQI is highly correlated with NO2 1st Max Hour and 7 other fieldsHigh correlation
SO2 Mean is highly correlated with SO2 1st Max Value and 1 other fieldsHigh correlation
SO2 1st Max Value is highly correlated with NO2 AQI and 4 other fieldsHigh correlation
CO Mean is highly correlated with NO2 Mean and 3 other fieldsHigh correlation
CO AQI is highly correlated with NO2 1st Max Hour and 4 other fieldsHigh correlation
NO2 1st Max Hour is highly correlated with NO2 AQI and 5 other fieldsHigh correlation
O3 1st Max Hour is highly correlated with NO2 1st Max Hour and 3 other fieldsHigh correlation
SO2 AQI has 3702 (50.0%) missing values Missing
CO AQI has 3703 (50.0%) missing values Missing
NO2 Mean is non stationary Non stationary
NO2 1st Max Value is non stationary Non stationary
NO2 1st Max Hour is non stationary Non stationary
NO2 AQI is non stationary Non stationary
O3 Mean is non stationary Non stationary
O3 1st Max Value is non stationary Non stationary
O3 AQI is non stationary Non stationary
SO2 Mean is non stationary Non stationary
SO2 1st Max Value is non stationary Non stationary
CO Mean is non stationary Non stationary
CO AQI is non stationary Non stationary
NO2 Mean is seasonal Seasonal
NO2 1st Max Value is seasonal Seasonal
NO2 1st Max Hour is seasonal Seasonal
NO2 AQI is seasonal Seasonal
O3 Mean is seasonal Seasonal
O3 1st Max Value is seasonal Seasonal
O3 AQI is seasonal Seasonal
SO2 Mean is seasonal Seasonal
SO2 1st Max Value is seasonal Seasonal
CO Mean is seasonal Seasonal
CO AQI is seasonal Seasonal
NO2 1st Max Hour has 612 (8.3%) zeros Zeros
SO2 Mean has 290 (3.9%) zeros Zeros
SO2 1st Max Value has 298 (4.0%) zeros Zeros
SO2 1st Max Hour has 288 (3.9%) zeros Zeros
SO2 AQI has 2596 (35.1%) zeros Zeros
CO 1st Max Hour has 2491 (33.6%) zeros Zeros

Reproduction

Analysis started2022-10-20 17:51:13.294934
Analysis finished2022-10-20 17:51:24.781988
Duration11.49 seconds
Software versionpandas-profiling v3.3.1
Download configurationconfig.json

Variables

State Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
4
7406 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7406
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
47406
100.0%

Length

2022-10-20T18:51:24.872890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Overview

Dataset statistics

Number of variables28
Number of observations7406
Missing cells7405
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory224.0 B

Variable types

Categorical11
DateTime1
TimeSeries12
Numeric4

Alerts

State Code has constant value "4" Constant
County Code has constant value "19" Constant
Site Num has constant value "1028" Constant
Address has constant value "400 W RIVER ROAD" Constant
State has constant value "Arizona" Constant
County has constant value "Pima" Constant
City has constant value "Tucson" Constant
NO2 Units has constant value "Parts per billion" Constant
O3 Units has constant value "Parts per million" Constant
SO2 Units has constant value "Parts per billion" Constant
CO Units has constant value "Parts per million" Constant
SO2 1st Max Hour is highly correlated with SO2 1st Max ValueHigh correlation
SO2 AQI is highly correlated with O3 1st Max Value and 2 other fieldsHigh correlation
CO 1st Max Value is highly correlated with NO2 AQI and 3 other fieldsHigh correlation
CO 1st Max Hour is highly correlated with NO2 1st Max HourHigh correlation
State Code is highly correlated with Address and 9 other fieldsHigh correlation
County Code is highly correlated with Address and 9 other fieldsHigh correlation
Site Num is highly correlated with Address and 9 other fieldsHigh correlation
Address is highly correlated with State and 9 other fieldsHigh correlation
State is highly correlated with Address and 9 other fieldsHigh correlation
County is highly correlated with Address and 9 other fieldsHigh correlation
City is highly correlated with Address and 9 other fieldsHigh correlation
NO2 Units is highly correlated with Address and 9 other fieldsHigh correlation
O3 Units is highly correlated with Address and 9 other fieldsHigh correlation
SO2 Units is highly correlated with Address and 9 other fieldsHigh correlation
CO Units is highly correlated with Address and 9 other fieldsHigh correlation
NO2 Mean is highly correlated with NO2 1st Max Value and 5 other fieldsHigh correlation
NO2 1st Max Value is highly correlated with NO2 Mean and 3 other fieldsHigh correlation
NO2 AQI is highly correlated with NO2 1st Max Hour and 6 other fieldsHigh correlation
O3 Mean is highly correlated with NO2 Mean and 4 other fieldsHigh correlation
O3 1st Max Value is highly correlated with NO2 1st Max Hour and 7 other fieldsHigh correlation
O3 AQI is highly correlated with NO2 1st Max Hour and 7 other fieldsHigh correlation
SO2 Mean is highly correlated with SO2 1st Max Value and 1 other fieldsHigh correlation
SO2 1st Max Value is highly correlated with NO2 AQI and 4 other fieldsHigh correlation
CO Mean is highly correlated with NO2 Mean and 3 other fieldsHigh correlation
CO AQI is highly correlated with NO2 1st Max Hour and 4 other fieldsHigh correlation
NO2 1st Max Hour is highly correlated with NO2 AQI and 5 other fieldsHigh correlation
O3 1st Max Hour is highly correlated with NO2 1st Max Hour and 3 other fieldsHigh correlation
SO2 AQI has 3702 (50.0%) missing values Missing
CO AQI has 3703 (50.0%) missing values Missing
NO2 Mean is non stationary Non stationary
NO2 1st Max Value is non stationary Non stationary
NO2 1st Max Hour is non stationary Non stationary
NO2 AQI is non stationary Non stationary
O3 Mean is non stationary Non stationary
O3 1st Max Value is non stationary Non stationary
O3 AQI is non stationary Non stationary
SO2 Mean is non stationary Non stationary
SO2 1st Max Value is non stationary Non stationary
CO Mean is non stationary Non stationary
CO AQI is non stationary Non stationary
NO2 Mean is seasonal Seasonal
NO2 1st Max Value is seasonal Seasonal
NO2 1st Max Hour is seasonal Seasonal
NO2 AQI is seasonal Seasonal
O3 Mean is seasonal Seasonal
O3 1st Max Value is seasonal Seasonal
O3 AQI is seasonal Seasonal
SO2 Mean is seasonal Seasonal
SO2 1st Max Value is seasonal Seasonal
CO Mean is seasonal Seasonal
CO AQI is seasonal Seasonal
NO2 1st Max Hour has 612 (8.3%) zeros Zeros
SO2 Mean has 290 (3.9%) zeros Zeros
SO2 1st Max Value has 298 (4.0%) zeros Zeros
SO2 1st Max Hour has 288 (3.9%) zeros Zeros
SO2 AQI has 2596 (35.1%) zeros Zeros
CO 1st Max Hour has 2491 (33.6%) zeros Zeros

Reproduction

Analysis started2022-10-20 18:29:58.947310
Analysis finished2022-10-20 18:30:05.926462
Duration6.98 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

State Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
4
7406 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7406
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
47406
100.0%

Length

2022-10-20T19:30:05.975787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:51:24.997961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:06.050748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
47406
100.0%

Most occurring characters

ValueCountFrequency (%)
47406
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7406
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
47406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7406
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
47406
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7406
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47406
100.0%

County Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
19
7406 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters14812
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row19
2nd row19
3rd row19
4th row19
5th row19

Common Values

ValueCountFrequency (%)
197406
100.0%

Length

2022-10-20T18:51:25.100419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
47406
100.0%

Most occurring characters

ValueCountFrequency (%)
47406
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7406
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
47406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7406
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
47406
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7406
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47406
100.0%

County Code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
19
7406 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters14812
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row19
2nd row19
3rd row19
4th row19
5th row19

Common Values

ValueCountFrequency (%)
197406
100.0%

Length

2022-10-20T19:30:06.116042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:51:25.224712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:06.186817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
197406
100.0%

Most occurring characters

ValueCountFrequency (%)
17406
50.0%
97406
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number14812
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
17406
50.0%
97406
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common14812
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
17406
50.0%
97406
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII14812
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17406
50.0%
97406
50.0%

Site Num
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
1028
7406 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters29624
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1028
2nd row1028
3rd row1028
4th row1028
5th row1028

Common Values

ValueCountFrequency (%)
10287406
100.0%

Length

2022-10-20T18:51:25.331145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
197406
100.0%

Most occurring characters

ValueCountFrequency (%)
17406
50.0%
97406
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number14812
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
17406
50.0%
97406
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common14812
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
17406
50.0%
97406
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII14812
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17406
50.0%
97406
50.0%

Site Num
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
1028
7406 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters29624
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1028
2nd row1028
3rd row1028
4th row1028
5th row1028

Common Values

ValueCountFrequency (%)
10287406
100.0%

Length

2022-10-20T19:30:06.248011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:51:25.456276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:06.318473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
10287406
100.0%

Most occurring characters

ValueCountFrequency (%)
17406
25.0%
07406
25.0%
27406
25.0%
87406
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number29624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
17406
25.0%
07406
25.0%
27406
25.0%
87406
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common29624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
17406
25.0%
07406
25.0%
27406
25.0%
87406
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII29624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17406
25.0%
07406
25.0%
27406
25.0%
87406
25.0%

Address
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
400 W RIVER ROAD
7406 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters118496
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row400 W RIVER ROAD
2nd row400 W RIVER ROAD
3rd row400 W RIVER ROAD
4th row400 W RIVER ROAD
5th row400 W RIVER ROAD

Common Values

ValueCountFrequency (%)
400 W RIVER ROAD7406
100.0%

Length

2022-10-20T18:51:25.574695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
10287406
100.0%

Most occurring characters

ValueCountFrequency (%)
17406
25.0%
07406
25.0%
27406
25.0%
87406
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number29624
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
17406
25.0%
07406
25.0%
27406
25.0%
87406
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common29624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
17406
25.0%
07406
25.0%
27406
25.0%
87406
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII29624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17406
25.0%
07406
25.0%
27406
25.0%
87406
25.0%

Address
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
400 W RIVER ROAD
7406 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters118496
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row400 W RIVER ROAD
2nd row400 W RIVER ROAD
3rd row400 W RIVER ROAD
4th row400 W RIVER ROAD
5th row400 W RIVER ROAD

Common Values

ValueCountFrequency (%)
400 W RIVER ROAD7406
100.0%

Length

2022-10-20T19:30:06.379588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:51:25.709819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:06.451581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4007406
25.0%
w7406
25.0%
river7406
25.0%
road7406
25.0%

Most occurring characters

ValueCountFrequency (%)
22218
18.8%
R22218
18.8%
014812
12.5%
47406
 
6.2%
W7406
 
6.2%
I7406
 
6.2%
V7406
 
6.2%
E7406
 
6.2%
O7406
 
6.2%
A7406
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter74060
62.5%
Space Separator22218
 
18.8%
Decimal Number22218
 
18.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R22218
30.0%
W7406
 
10.0%
I7406
 
10.0%
V7406
 
10.0%
E7406
 
10.0%
O7406
 
10.0%
A7406
 
10.0%
D7406
 
10.0%
Decimal Number
ValueCountFrequency (%)
014812
66.7%
47406
33.3%
Space Separator
ValueCountFrequency (%)
22218
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin74060
62.5%
Common44436
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
R22218
30.0%
W7406
 
10.0%
I7406
 
10.0%
V7406
 
10.0%
E7406
 
10.0%
O7406
 
10.0%
A7406
 
10.0%
D7406
 
10.0%
Common
ValueCountFrequency (%)
22218
50.0%
014812
33.3%
47406
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII118496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22218
18.8%
R22218
18.8%
014812
12.5%
47406
 
6.2%
W7406
 
6.2%
I7406
 
6.2%
V7406
 
6.2%
E7406
 
6.2%
O7406
 
6.2%
A7406
 
6.2%

State
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
Arizona
7406 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters51842
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
Arizona7406
100.0%

Length

2022-10-20T18:51:25.826189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4007406
25.0%
w7406
25.0%
river7406
25.0%
road7406
25.0%

Most occurring characters

ValueCountFrequency (%)
22218
18.8%
R22218
18.8%
014812
12.5%
47406
 
6.2%
W7406
 
6.2%
I7406
 
6.2%
V7406
 
6.2%
E7406
 
6.2%
O7406
 
6.2%
A7406
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter74060
62.5%
Space Separator22218
 
18.8%
Decimal Number22218
 
18.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R22218
30.0%
W7406
 
10.0%
I7406
 
10.0%
V7406
 
10.0%
E7406
 
10.0%
O7406
 
10.0%
A7406
 
10.0%
D7406
 
10.0%
Decimal Number
ValueCountFrequency (%)
014812
66.7%
47406
33.3%
Space Separator
ValueCountFrequency (%)
22218
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin74060
62.5%
Common44436
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
R22218
30.0%
W7406
 
10.0%
I7406
 
10.0%
V7406
 
10.0%
E7406
 
10.0%
O7406
 
10.0%
A7406
 
10.0%
D7406
 
10.0%
Common
ValueCountFrequency (%)
22218
50.0%
014812
33.3%
47406
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII118496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22218
18.8%
R22218
18.8%
014812
12.5%
47406
 
6.2%
W7406
 
6.2%
I7406
 
6.2%
V7406
 
6.2%
E7406
 
6.2%
O7406
 
6.2%
A7406
 
6.2%

State
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
Arizona
7406 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters51842
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArizona
2nd rowArizona
3rd rowArizona
4th rowArizona
5th rowArizona

Common Values

ValueCountFrequency (%)
Arizona7406
100.0%

Length

2022-10-20T19:30:06.515221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:51:25.980326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:06.591285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
arizona7406
100.0%

Most occurring characters

ValueCountFrequency (%)
A7406
14.3%
r7406
14.3%
i7406
14.3%
z7406
14.3%
o7406
14.3%
n7406
14.3%
a7406
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter44436
85.7%
Uppercase Letter7406
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r7406
16.7%
i7406
16.7%
z7406
16.7%
o7406
16.7%
n7406
16.7%
a7406
16.7%
Uppercase Letter
ValueCountFrequency (%)
A7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin51842
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A7406
14.3%
r7406
14.3%
i7406
14.3%
z7406
14.3%
o7406
14.3%
n7406
14.3%
a7406
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII51842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A7406
14.3%
r7406
14.3%
i7406
14.3%
z7406
14.3%
o7406
14.3%
n7406
14.3%
a7406
14.3%

County
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
Pima
7406 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters29624
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPima
2nd rowPima
3rd rowPima
4th rowPima
5th rowPima

Common Values

ValueCountFrequency (%)
Pima7406
100.0%

Length

2022-10-20T18:51:26.086708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
arizona7406
100.0%

Most occurring characters

ValueCountFrequency (%)
A7406
14.3%
r7406
14.3%
i7406
14.3%
z7406
14.3%
o7406
14.3%
n7406
14.3%
a7406
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter44436
85.7%
Uppercase Letter7406
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r7406
16.7%
i7406
16.7%
z7406
16.7%
o7406
16.7%
n7406
16.7%
a7406
16.7%
Uppercase Letter
ValueCountFrequency (%)
A7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin51842
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A7406
14.3%
r7406
14.3%
i7406
14.3%
z7406
14.3%
o7406
14.3%
n7406
14.3%
a7406
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII51842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A7406
14.3%
r7406
14.3%
i7406
14.3%
z7406
14.3%
o7406
14.3%
n7406
14.3%
a7406
14.3%

County
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
Pima
7406 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters29624
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPima
2nd rowPima
3rd rowPima
4th rowPima
5th rowPima

Common Values

ValueCountFrequency (%)
Pima7406
100.0%

Length

2022-10-20T19:30:06.652865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:51:26.210248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:06.723998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
pima7406
100.0%

Most occurring characters

ValueCountFrequency (%)
P7406
25.0%
i7406
25.0%
m7406
25.0%
a7406
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter22218
75.0%
Uppercase Letter7406
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i7406
33.3%
m7406
33.3%
a7406
33.3%
Uppercase Letter
ValueCountFrequency (%)
P7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin29624
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P7406
25.0%
i7406
25.0%
m7406
25.0%
a7406
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII29624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P7406
25.0%
i7406
25.0%
m7406
25.0%
a7406
25.0%

City
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
Tucson
7406 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters44436
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTucson
2nd rowTucson
3rd rowTucson
4th rowTucson
5th rowTucson

Common Values

ValueCountFrequency (%)
Tucson7406
100.0%

Length

2022-10-20T18:51:26.327175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
pima7406
100.0%

Most occurring characters

ValueCountFrequency (%)
P7406
25.0%
i7406
25.0%
m7406
25.0%
a7406
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter22218
75.0%
Uppercase Letter7406
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i7406
33.3%
m7406
33.3%
a7406
33.3%
Uppercase Letter
ValueCountFrequency (%)
P7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin29624
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P7406
25.0%
i7406
25.0%
m7406
25.0%
a7406
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII29624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P7406
25.0%
i7406
25.0%
m7406
25.0%
a7406
25.0%

City
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
Tucson
7406 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters44436
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTucson
2nd rowTucson
3rd rowTucson
4th rowTucson
5th rowTucson

Common Values

ValueCountFrequency (%)
Tucson7406
100.0%

Length

2022-10-20T19:30:06.784597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:51:26.456131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:06.857568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
tucson7406
100.0%

Most occurring characters

ValueCountFrequency (%)
T7406
16.7%
u7406
16.7%
c7406
16.7%
s7406
16.7%
o7406
16.7%
n7406
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter37030
83.3%
Uppercase Letter7406
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u7406
20.0%
c7406
20.0%
s7406
20.0%
o7406
20.0%
n7406
20.0%
Uppercase Letter
ValueCountFrequency (%)
T7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin44436
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T7406
16.7%
u7406
16.7%
c7406
16.7%
s7406
16.7%
o7406
16.7%
n7406
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII44436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T7406
16.7%
u7406
16.7%
c7406
16.7%
s7406
16.7%
o7406
16.7%
n7406
16.7%
Distinct1852
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
Minimum2010-10-01 00:00:00
Maximum2016-03-31 00:00:00
2022-10-20T18:51:26.581127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
tucson7406
100.0%

Most occurring characters

ValueCountFrequency (%)
T7406
16.7%
u7406
16.7%
c7406
16.7%
s7406
16.7%
o7406
16.7%
n7406
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter37030
83.3%
Uppercase Letter7406
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u7406
20.0%
c7406
20.0%
s7406
20.0%
o7406
20.0%
n7406
20.0%
Uppercase Letter
ValueCountFrequency (%)
T7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin44436
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T7406
16.7%
u7406
16.7%
c7406
16.7%
s7406
16.7%
o7406
16.7%
n7406
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII44436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T7406
16.7%
u7406
16.7%
c7406
16.7%
s7406
16.7%
o7406
16.7%
n7406
16.7%
Distinct1852
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
Minimum2010-10-01 00:00:00
Maximum2016-03-31 00:00:00
2022-10-20T19:30:06.933832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:51:26.746355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:30:07.034066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
Parts per billion
7406 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters125902
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion7406
100.0%

Length

2022-10-20T18:51:26.890157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

NO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
Parts per billion
7406 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters125902
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion7406
100.0%

Length

2022-10-20T19:30:07.117171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:51:27.013853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:07.199328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts7406
33.3%
per7406
33.3%
billion7406
33.3%

Most occurring characters

ValueCountFrequency (%)
r14812
11.8%
14812
11.8%
i14812
11.8%
l14812
11.8%
P7406
 
5.9%
a7406
 
5.9%
t7406
 
5.9%
s7406
 
5.9%
p7406
 
5.9%
e7406
 
5.9%
Other values (3)22218
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter103684
82.4%
Space Separator14812
 
11.8%
Uppercase Letter7406
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r14812
14.3%
i14812
14.3%
l14812
14.3%
a7406
7.1%
t7406
7.1%
s7406
7.1%
p7406
7.1%
e7406
7.1%
b7406
7.1%
o7406
7.1%
Space Separator
ValueCountFrequency (%)
14812
100.0%
Uppercase Letter
ValueCountFrequency (%)
P7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin111090
88.2%
Common14812
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r14812
13.3%
i14812
13.3%
l14812
13.3%
P7406
6.7%
a7406
6.7%
t7406
6.7%
s7406
6.7%
p7406
6.7%
e7406
6.7%
b7406
6.7%
Other values (2)14812
13.3%
Common
ValueCountFrequency (%)
14812
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII125902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r14812
11.8%
14812
11.8%
i14812
11.8%
l14812
11.8%
P7406
 
5.9%
a7406
 
5.9%
t7406
 
5.9%
s7406
 
5.9%
p7406
 
5.9%
e7406
 
5.9%
Other values (3)22218
17.6%

NO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1610
Distinct (%)0.21739130434782608
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean11.371198702673508
Minimum0.834783
Maximum29.409524
Zeros0
Zeros (%)0.0
Memory size59376
2022-10-20T18:51:27.115515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts7406
33.3%
per7406
33.3%
billion7406
33.3%

Most occurring characters

ValueCountFrequency (%)
r14812
11.8%
14812
11.8%
i14812
11.8%
l14812
11.8%
P7406
 
5.9%
a7406
 
5.9%
t7406
 
5.9%
s7406
 
5.9%
p7406
 
5.9%
e7406
 
5.9%
Other values (3)22218
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter103684
82.4%
Space Separator14812
 
11.8%
Uppercase Letter7406
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r14812
14.3%
i14812
14.3%
l14812
14.3%
a7406
7.1%
t7406
7.1%
s7406
7.1%
p7406
7.1%
e7406
7.1%
b7406
7.1%
o7406
7.1%
Space Separator
ValueCountFrequency (%)
14812
100.0%
Uppercase Letter
ValueCountFrequency (%)
P7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin111090
88.2%
Common14812
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r14812
13.3%
i14812
13.3%
l14812
13.3%
P7406
6.7%
a7406
6.7%
t7406
6.7%
s7406
6.7%
p7406
6.7%
e7406
6.7%
b7406
6.7%
Other values (2)14812
13.3%
Common
ValueCountFrequency (%)
14812
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII125902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r14812
11.8%
14812
11.8%
i14812
11.8%
l14812
11.8%
P7406
 
5.9%
a7406
 
5.9%
t7406
 
5.9%
s7406
 
5.9%
p7406
 
5.9%
e7406
 
5.9%
Other values (3)22218
17.6%

NO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct1610
Distinct (%)0.21739130434782608
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean11.371198702673508
Minimum0.834783
Maximum29.409524
Zeros0
Zeros (%)0.0
Memory size59376
2022-10-20T19:30:07.261435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.834783
5-th percentile3.883333
Q16.90104175
median10.175
Q315.31331525
95-th percentile21.8375
Maximum29.409524
Range28.574741
Interquartile range (IQR)8.4122735

Descriptive statistics

Standard deviation5.616530379
Coefficient of variation (CV)0.4939259726
Kurtosis-0.46198764
Mean11.3711987
Median Absolute Deviation (MAD)3.820833
Skewness0.5972068972
Sum84215.09759
Variance31.5454135
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0001170396581
2022-10-20T18:51:27.283971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.834783
5-th percentile3.883333
Q16.90104175
median10.175
Q315.31331525
95-th percentile21.8375
Maximum29.409524
Range28.574741
Interquartile range (IQR)8.4122735

Descriptive statistics

Standard deviation5.616530379
Coefficient of variation (CV)0.4939259726
Kurtosis-0.46198764
Mean11.3711987
Median Absolute Deviation (MAD)3.820833
Skewness0.5972068972
Sum84215.09759
Variance31.5454135
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0001170396581
2022-10-20T19:30:07.358352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.2516
 
0.2%
5.28333316
 
0.2%
7.34583316
 
0.2%
6.78333316
 
0.2%
5.67083312
 
0.2%
11.20416712
 
0.2%
8.83333312
 
0.2%
9.12916712
 
0.2%
13.27512
 
0.2%
16.62916712
 
0.2%
Other values (1600)7270
98.2%
ValueCountFrequency (%)
0.8347834
0.1%
1.46254
0.1%
1.7666674
0.1%
1.7708334
0.1%
1.78754
0.1%
1.81254
0.1%
1.9166674
0.1%
1.9583334
0.1%
2.0041674
0.1%
2.0083334
0.1%
ValueCountFrequency (%)
29.4095244
0.1%
28.854
0.1%
28.21254
0.1%
27.88754
0.1%
27.3254
0.1%
26.91254
0.1%
26.7541674
0.1%
26.5083334
0.1%
26.2666674
0.1%
26.054
0.1%
2022-10-20T18:51:27.552746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.2516
 
0.2%
5.28333316
 
0.2%
7.34583316
 
0.2%
6.78333316
 
0.2%
5.67083312
 
0.2%
11.20416712
 
0.2%
8.83333312
 
0.2%
9.12916712
 
0.2%
13.27512
 
0.2%
16.62916712
 
0.2%
Other values (1600)7270
98.2%
ValueCountFrequency (%)
0.8347834
0.1%
1.46254
0.1%
1.7666674
0.1%
1.7708334
0.1%
1.78754
0.1%
1.81254
0.1%
1.9166674
0.1%
1.9583334
0.1%
2.0041674
0.1%
2.0083334
0.1%
ValueCountFrequency (%)
29.4095244
0.1%
28.854
0.1%
28.21254
0.1%
27.88754
0.1%
27.3254
0.1%
26.91254
0.1%
26.7541674
0.1%
26.5083334
0.1%
26.2666674
0.1%
26.054
0.1%
2022-10-20T19:30:07.563391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct378
Distinct (%)0.05103969754253308
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean24.410423980556306
Minimum3.7
Maximum46.4
Zeros0
Zeros (%)0.0
Memory size59376
2022-10-20T18:51:27.774038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct378
Distinct (%)0.05103969754253308
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean24.410423980556306
Minimum3.7
Maximum46.4
Zeros0
Zeros (%)0.0
Memory size59376
2022-10-20T19:30:07.690026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.7
5-th percentile9.6
Q117.5
median24.5
Q331.2
95-th percentile38.6
Maximum46.4
Range42.7
Interquartile range (IQR)13.7

Descriptive statistics

Standard deviation8.97176421
Coefficient of variation (CV)0.3675382376
Kurtosis-0.7361843318
Mean24.41042398
Median Absolute Deviation (MAD)6.8
Skewness-0.03398524538
Sum180783.6
Variance80.49255304
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.793613435 × 10-6
2022-10-20T18:51:27.928398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.7
5-th percentile9.6
Q117.5
median24.5
Q331.2
95-th percentile38.6
Maximum46.4
Range42.7
Interquartile range (IQR)13.7

Descriptive statistics

Standard deviation8.97176421
Coefficient of variation (CV)0.3675382376
Kurtosis-0.7361843318
Mean24.41042398
Median Absolute Deviation (MAD)6.8
Skewness-0.03398524538
Sum180783.6
Variance80.49255304
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.793613435 × 10-6
2022-10-20T19:30:07.783784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2856
 
0.8%
17.556
 
0.8%
28.652
 
0.7%
29.352
 
0.7%
3348
 
0.6%
3148
 
0.6%
23.248
 
0.6%
21.348
 
0.6%
15.548
 
0.6%
33.744
 
0.6%
Other values (368)6906
93.2%
ValueCountFrequency (%)
3.74
0.1%
3.84
0.1%
4.28
0.1%
4.34
0.1%
4.54
0.1%
4.68
0.1%
4.84
0.1%
4.94
0.1%
54
0.1%
5.24
0.1%
ValueCountFrequency (%)
46.44
0.1%
46.24
0.1%
46.14
0.1%
45.84
0.1%
45.74
0.1%
45.64
0.1%
45.54
0.1%
45.38
0.1%
45.14
0.1%
458
0.1%
2022-10-20T18:51:28.178066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2856
 
0.8%
17.556
 
0.8%
28.652
 
0.7%
29.352
 
0.7%
3348
 
0.6%
3148
 
0.6%
23.248
 
0.6%
21.348
 
0.6%
15.548
 
0.6%
33.744
 
0.6%
Other values (368)6906
93.2%
ValueCountFrequency (%)
3.74
0.1%
3.84
0.1%
4.28
0.1%
4.34
0.1%
4.54
0.1%
4.68
0.1%
4.84
0.1%
4.94
0.1%
54
0.1%
5.24
0.1%
ValueCountFrequency (%)
46.44
0.1%
46.24
0.1%
46.14
0.1%
45.84
0.1%
45.74
0.1%
45.64
0.1%
45.54
0.1%
45.38
0.1%
45.14
0.1%
458
0.1%
2022-10-20T19:30:07.959290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct24
Distinct (%)0.0032406157169862274
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean13.455036456926816
Minimum0
Maximum23
Zeros612
Zeros (%)0.0826357007831488
Memory size59376
2022-10-20T18:51:28.478925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 1st Max Hour
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct24
Distinct (%)0.0032406157169862274
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean13.455036456926816
Minimum0
Maximum23
Zeros612
Zeros (%)0.0826357007831488
Memory size59376
2022-10-20T19:30:08.086628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median18
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.055349774
Coefficient of variation (CV)0.598686581
Kurtosis-1.541095125
Mean13.45503646
Median Absolute Deviation (MAD)5
Skewness-0.2883018041
Sum99648
Variance64.88865998
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.555712396 × 10-14
2022-10-20T18:51:28.606548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median18
Q321
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.055349774
Coefficient of variation (CV)0.598686581
Kurtosis-1.541095125
Mean13.45503646
Median Absolute Deviation (MAD)5
Skewness-0.2883018041
Sum99648
Variance64.88865998
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.555712396 × 10-14
2022-10-20T19:30:08.166485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
21956
12.9%
7764
10.3%
22748
10.1%
20716
9.7%
6698
9.4%
0612
8.3%
19564
7.6%
23540
7.3%
8464
6.3%
18340
 
4.6%
Other values (14)1004
13.6%
ValueCountFrequency (%)
0612
8.3%
1168
 
2.3%
284
 
1.1%
360
 
0.8%
440
 
0.5%
5264
 
3.6%
6698
9.4%
7764
10.3%
8464
6.3%
9200
 
2.7%
ValueCountFrequency (%)
23540
7.3%
22748
10.1%
21956
12.9%
20716
9.7%
19564
7.6%
18340
 
4.6%
1772
 
1.0%
164
 
0.1%
154
 
0.1%
144
 
0.1%
2022-10-20T18:51:28.867492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
21956
12.9%
7764
10.3%
22748
10.1%
20716
9.7%
6698
9.4%
0612
8.3%
19564
7.6%
23540
7.3%
8464
6.3%
18340
 
4.6%
Other values (14)1004
13.6%
ValueCountFrequency (%)
0612
8.3%
1168
 
2.3%
284
 
1.1%
360
 
0.8%
440
 
0.5%
5264
 
3.6%
6698
9.4%
7764
10.3%
8464
6.3%
9200
 
2.7%
ValueCountFrequency (%)
23540
7.3%
22748
10.1%
21956
12.9%
20716
9.7%
19564
7.6%
18340
 
4.6%
1772
 
1.0%
164
 
0.1%
154
 
0.1%
144
 
0.1%
2022-10-20T19:30:08.331177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct41
Distinct (%)0.0055360518498514715
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean22.577639751552795
Minimum3
Maximum43
Zeros0
Zeros (%)0.0
Memory size59376
2022-10-20T18:51:29.125047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

NO2 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct41
Distinct (%)0.0055360518498514715
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean22.577639751552795
Minimum3
Maximum43
Zeros0
Zeros (%)0.0
Memory size59376
2022-10-20T19:30:08.464506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8
Q116
median23
Q329
95-th percentile36
Maximum43
Range40
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.502697329
Coefficient of variation (CV)0.3765981485
Kurtosis-0.7068879843
Mean22.57763975
Median Absolute Deviation (MAD)6
Skewness-0.04048374055
Sum167210
Variance72.29586187
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.849453197 × 10-6
2022-10-20T18:51:29.292758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8
Q116
median23
Q329
95-th percentile36
Maximum43
Range40
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.502697329
Coefficient of variation (CV)0.3765981485
Kurtosis-0.7068879843
Mean22.57763975
Median Absolute Deviation (MAD)6
Skewness-0.04048374055
Sum167210
Variance72.29586187
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.849453197 × 10-6
2022-10-20T19:30:08.566193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
25600
 
8.1%
26320
 
4.3%
22316
 
4.3%
21292
 
3.9%
27284
 
3.8%
29284
 
3.8%
20276
 
3.7%
17272
 
3.7%
31272
 
3.7%
18264
 
3.6%
Other values (31)4226
57.1%
ValueCountFrequency (%)
38
 
0.1%
432
 
0.4%
536
 
0.5%
640
 
0.5%
780
 
1.1%
8216
2.9%
9120
1.6%
10196
2.6%
11172
2.3%
12196
2.6%
ValueCountFrequency (%)
4312
 
0.2%
4252
 
0.7%
4124
 
0.3%
4032
 
0.4%
3972
1.0%
3844
 
0.6%
37100
1.4%
3692
1.2%
35156
2.1%
34172
2.3%
2022-10-20T18:51:29.562252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
25600
 
8.1%
26320
 
4.3%
22316
 
4.3%
21292
 
3.9%
27284
 
3.8%
29284
 
3.8%
20276
 
3.7%
17272
 
3.7%
31272
 
3.7%
18264
 
3.6%
Other values (31)4226
57.1%
ValueCountFrequency (%)
38
 
0.1%
432
 
0.4%
536
 
0.5%
640
 
0.5%
780
 
1.1%
8216
2.9%
9120
1.6%
10196
2.6%
11172
2.3%
12196
2.6%
ValueCountFrequency (%)
4312
 
0.2%
4252
 
0.7%
4124
 
0.3%
4032
 
0.4%
3972
1.0%
3844
 
0.6%
37100
1.4%
3692
1.2%
35156
2.1%
34172
2.3%
2022-10-20T19:30:08.829327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
Parts per million
7406 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters125902
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million7406
100.0%

Length

2022-10-20T18:51:29.790072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
Parts per million
7406 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters125902
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million7406
100.0%

Length

2022-10-20T19:30:09.386241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:51:30.246713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:09.503953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts7406
33.3%
per7406
33.3%
million7406
33.3%

Most occurring characters

ValueCountFrequency (%)
r14812
11.8%
14812
11.8%
i14812
11.8%
l14812
11.8%
P7406
 
5.9%
a7406
 
5.9%
t7406
 
5.9%
s7406
 
5.9%
p7406
 
5.9%
e7406
 
5.9%
Other values (3)22218
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter103684
82.4%
Space Separator14812
 
11.8%
Uppercase Letter7406
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r14812
14.3%
i14812
14.3%
l14812
14.3%
a7406
7.1%
t7406
7.1%
s7406
7.1%
p7406
7.1%
e7406
7.1%
m7406
7.1%
o7406
7.1%
Space Separator
ValueCountFrequency (%)
14812
100.0%
Uppercase Letter
ValueCountFrequency (%)
P7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin111090
88.2%
Common14812
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r14812
13.3%
i14812
13.3%
l14812
13.3%
P7406
6.7%
a7406
6.7%
t7406
6.7%
s7406
6.7%
p7406
6.7%
e7406
6.7%
m7406
6.7%
Other values (2)14812
13.3%
Common
ValueCountFrequency (%)
14812
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII125902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r14812
11.8%
14812
11.8%
i14812
11.8%
l14812
11.8%
P7406
 
5.9%
a7406
 
5.9%
t7406
 
5.9%
s7406
 
5.9%
p7406
 
5.9%
e7406
 
5.9%
Other values (3)22218
17.6%

O3 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct841
Distinct (%)0.11355657574939239
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.027341289495004053
Minimum0.003125
Maximum0.059167
Zeros0
Zeros (%)0.0
Memory size59376
2022-10-20T18:51:30.353701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts7406
33.3%
per7406
33.3%
million7406
33.3%

Most occurring characters

ValueCountFrequency (%)
r14812
11.8%
14812
11.8%
i14812
11.8%
l14812
11.8%
P7406
 
5.9%
a7406
 
5.9%
t7406
 
5.9%
s7406
 
5.9%
p7406
 
5.9%
e7406
 
5.9%
Other values (3)22218
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter103684
82.4%
Space Separator14812
 
11.8%
Uppercase Letter7406
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r14812
14.3%
i14812
14.3%
l14812
14.3%
a7406
7.1%
t7406
7.1%
s7406
7.1%
p7406
7.1%
e7406
7.1%
m7406
7.1%
o7406
7.1%
Space Separator
ValueCountFrequency (%)
14812
100.0%
Uppercase Letter
ValueCountFrequency (%)
P7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin111090
88.2%
Common14812
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r14812
13.3%
i14812
13.3%
l14812
13.3%
P7406
6.7%
a7406
6.7%
t7406
6.7%
s7406
6.7%
p7406
6.7%
e7406
6.7%
m7406
6.7%
Other values (2)14812
13.3%
Common
ValueCountFrequency (%)
14812
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII125902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r14812
11.8%
14812
11.8%
i14812
11.8%
l14812
11.8%
P7406
 
5.9%
a7406
 
5.9%
t7406
 
5.9%
s7406
 
5.9%
p7406
 
5.9%
e7406
 
5.9%
Other values (3)22218
17.6%

O3 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct841
Distinct (%)0.11355657574939239
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.027341289495004053
Minimum0.003125
Maximum0.059167
Zeros0
Zeros (%)0.0
Memory size59376
2022-10-20T19:30:09.603490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.003125
5-th percentile0.011
Q10.018667
median0.027375
Q30.03525
95-th percentile0.0445
Maximum0.059167
Range0.056042
Interquartile range (IQR)0.016583

Descriptive statistics

Standard deviation0.01050445569
Coefficient of variation (CV)0.3841975228
Kurtosis-0.7360837404
Mean0.0273412895
Median Absolute Deviation (MAD)0.008167
Skewness0.1270622722
Sum202.48959
Variance0.0001103435894
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.003658420953
2022-10-20T18:51:30.517931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.003125
5-th percentile0.011
Q10.018667
median0.027375
Q30.03525
95-th percentile0.0445
Maximum0.059167
Range0.056042
Interquartile range (IQR)0.016583

Descriptive statistics

Standard deviation0.01050445569
Coefficient of variation (CV)0.3841975228
Kurtosis-0.7360837404
Mean0.0273412895
Median Absolute Deviation (MAD)0.008167
Skewness0.1270622722
Sum202.48959
Variance0.0001103435894
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.003658420953
2022-10-20T19:30:09.743808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02079232
 
0.4%
0.03091732
 
0.4%
0.02895832
 
0.4%
0.03058328
 
0.4%
0.03558324
 
0.3%
0.01866724
 
0.3%
0.02508324
 
0.3%
0.0137524
 
0.3%
0.02854224
 
0.3%
0.03762524
 
0.3%
Other values (831)7138
96.4%
ValueCountFrequency (%)
0.0031254
0.1%
0.0040424
0.1%
0.0041674
0.1%
0.00554
0.1%
0.0064
0.1%
0.0060424
0.1%
0.0060838
0.1%
0.0061254
0.1%
0.0063754
0.1%
0.0074174
0.1%
ValueCountFrequency (%)
0.0591674
0.1%
0.0589174
0.1%
0.0568754
0.1%
0.0564
0.1%
0.054254
0.1%
0.0533334
0.1%
0.0531674
0.1%
0.0529584
0.1%
0.0523334
0.1%
0.0521254
0.1%
2022-10-20T18:51:30.963585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02079232
 
0.4%
0.03091732
 
0.4%
0.02895832
 
0.4%
0.03058328
 
0.4%
0.03558324
 
0.3%
0.01866724
 
0.3%
0.02508324
 
0.3%
0.0137524
 
0.3%
0.02854224
 
0.3%
0.03762524
 
0.3%
Other values (831)7138
96.4%
ValueCountFrequency (%)
0.0031254
0.1%
0.0040424
0.1%
0.0041674
0.1%
0.00554
0.1%
0.0064
0.1%
0.0060424
0.1%
0.0060838
0.1%
0.0061254
0.1%
0.0063754
0.1%
0.0074174
0.1%
ValueCountFrequency (%)
0.0591674
0.1%
0.0589174
0.1%
0.0568754
0.1%
0.0564
0.1%
0.054254
0.1%
0.0533334
0.1%
0.0531674
0.1%
0.0529584
0.1%
0.0523334
0.1%
0.0521254
0.1%
2022-10-20T19:30:10.024061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct65
Distinct (%)0.008776667566837698
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.04275668376991629
Minimum0.008
Maximum0.077
Zeros0
Zeros (%)0.0
Memory size59376
2022-10-20T18:51:31.268493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct65
Distinct (%)0.008776667566837698
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.04275668376991629
Minimum0.008
Maximum0.077
Zeros0
Zeros (%)0.0
Memory size59376
2022-10-20T19:30:10.212383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.026
Q10.035
median0.043
Q30.05
95-th percentile0.061
Maximum0.077
Range0.069
Interquartile range (IQR)0.015

Descriptive statistics

Standard deviation0.01045428325
Coefficient of variation (CV)0.2445064101
Kurtosis-0.2653198446
Mean0.04275668377
Median Absolute Deviation (MAD)0.007
Skewness0.06642658712
Sum316.656
Variance0.0001092920384
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0003248964579
2022-10-20T18:51:31.464125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.026
Q10.035
median0.043
Q30.05
95-th percentile0.061
Maximum0.077
Range0.069
Interquartile range (IQR)0.015

Descriptive statistics

Standard deviation0.01045428325
Coefficient of variation (CV)0.2445064101
Kurtosis-0.2653198446
Mean0.04275668377
Median Absolute Deviation (MAD)0.007
Skewness0.06642658712
Sum316.656
Variance0.0001092920384
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0003248964579
2022-10-20T19:30:10.352186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.043328
 
4.4%
0.038322
 
4.3%
0.046292
 
3.9%
0.041272
 
3.7%
0.034272
 
3.7%
0.047268
 
3.6%
0.048256
 
3.5%
0.049256
 
3.5%
0.035248
 
3.3%
0.044240
 
3.2%
Other values (55)4652
62.8%
ValueCountFrequency (%)
0.0084
 
0.1%
0.014
 
0.1%
0.0128
 
0.1%
0.0144
 
0.1%
0.0154
 
0.1%
0.0168
 
0.1%
0.0178
 
0.1%
0.0188
 
0.1%
0.0198
 
0.1%
0.0220
0.3%
ValueCountFrequency (%)
0.0774
 
0.1%
0.0754
 
0.1%
0.0744
 
0.1%
0.0734
 
0.1%
0.0724
 
0.1%
0.0714
 
0.1%
0.0698
 
0.1%
0.06812
 
0.2%
0.0678
 
0.1%
0.06640
0.5%
2022-10-20T18:51:31.817855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.043328
 
4.4%
0.038322
 
4.3%
0.046292
 
3.9%
0.041272
 
3.7%
0.034272
 
3.7%
0.047268
 
3.6%
0.048256
 
3.5%
0.049256
 
3.5%
0.035248
 
3.3%
0.044240
 
3.2%
Other values (55)4652
62.8%
ValueCountFrequency (%)
0.0084
 
0.1%
0.014
 
0.1%
0.0128
 
0.1%
0.0144
 
0.1%
0.0154
 
0.1%
0.0168
 
0.1%
0.0178
 
0.1%
0.0188
 
0.1%
0.0198
 
0.1%
0.0220
0.3%
ValueCountFrequency (%)
0.0774
 
0.1%
0.0754
 
0.1%
0.0744
 
0.1%
0.0734
 
0.1%
0.0724
 
0.1%
0.0714
 
0.1%
0.0698
 
0.1%
0.06812
 
0.2%
0.0678
 
0.1%
0.06640
0.5%
2022-10-20T19:30:10.560504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Hour
Numeric time series

HIGH CORRELATION

Distinct21
Distinct (%)0.002835538752362949
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean10.210369970294355
Minimum0
Maximum23
Zeros68
Zeros (%)0.009181744531460978
Memory size59376
2022-10-20T18:51:32.062979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 1st Max Hour
Numeric time series

HIGH CORRELATION

Distinct21
Distinct (%)0.002835538752362949
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean10.210369970294355
Minimum0
Maximum23
Zeros68
Zeros (%)0.009181744531460978
Memory size59376
2022-10-20T19:30:10.690146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q110
median10
Q311
95-th percentile12
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.897232911
Coefficient of variation (CV)0.1858143159
Kurtosis17.49258774
Mean10.21036997
Median Absolute Deviation (MAD)1
Skewness0.5346047694
Sum75618
Variance3.599492719
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.583869238 × 10-23
2022-10-20T18:51:32.196898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q110
median10
Q311
95-th percentile12
Maximum23
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.897232911
Coefficient of variation (CV)0.1858143159
Kurtosis17.49258774
Mean10.21036997
Median Absolute Deviation (MAD)1
Skewness0.5346047694
Sum75618
Variance3.599492719
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.583869238 × 10-23
2022-10-20T19:30:10.766846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
103080
41.6%
112010
27.1%
91396
18.8%
12296
 
4.0%
8188
 
2.5%
1476
 
1.0%
1372
 
1.0%
068
 
0.9%
740
 
0.5%
1528
 
0.4%
Other values (11)152
 
2.1%
ValueCountFrequency (%)
068
 
0.9%
212
 
0.2%
48
 
0.1%
616
 
0.2%
740
 
0.5%
8188
 
2.5%
91396
18.8%
103080
41.6%
112010
27.1%
12296
 
4.0%
ValueCountFrequency (%)
2316
 
0.2%
228
 
0.1%
2112
 
0.2%
2016
 
0.2%
1920
 
0.3%
1812
 
0.2%
1712
 
0.2%
1620
 
0.3%
1528
 
0.4%
1476
1.0%
2022-10-20T18:51:32.481176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
103080
41.6%
112010
27.1%
91396
18.8%
12296
 
4.0%
8188
 
2.5%
1476
 
1.0%
1372
 
1.0%
068
 
0.9%
740
 
0.5%
1528
 
0.4%
Other values (11)152
 
2.1%
ValueCountFrequency (%)
068
 
0.9%
212
 
0.2%
48
 
0.1%
616
 
0.2%
740
 
0.5%
8188
 
2.5%
91396
18.8%
103080
41.6%
112010
27.1%
12296
 
4.0%
ValueCountFrequency (%)
2316
 
0.2%
228
 
0.1%
2112
 
0.2%
2016
 
0.2%
1920
 
0.3%
1812
 
0.2%
1712
 
0.2%
1620
 
0.3%
1528
 
0.4%
1476
1.0%
2022-10-20T19:30:10.947842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct58
Distinct (%)0.007831487982716717
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean38.35889819065623
Minimum7
Maximum122
Zeros0
Zeros (%)0.0
Memory size59376
2022-10-20T18:51:32.722267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

O3 AQI
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct58
Distinct (%)0.007831487982716717
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean38.35889819065623
Minimum7
Maximum122
Zeros0
Zeros (%)0.0
Memory size59376
2022-10-20T19:30:11.075951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile23
Q131
median37
Q344
95-th percentile58
Maximum122
Range115
Interquartile range (IQR)13

Descriptive statistics

Standard deviation11.38619257
Coefficient of variation (CV)0.2968331498
Kurtosis4.113322058
Mean38.35889819
Median Absolute Deviation (MAD)6
Skewness1.269395517
Sum284086
Variance129.6453813
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.635888185 × 10-5
2022-10-20T18:51:32.881611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile23
Q131
median37
Q344
95-th percentile58
Maximum122
Range115
Interquartile range (IQR)13

Descriptive statistics

Standard deviation11.38619257
Coefficient of variation (CV)0.2968331498
Kurtosis4.113322058
Mean38.35889819
Median Absolute Deviation (MAD)6
Skewness1.269395517
Sum284086
Variance129.6453813
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.635888185 × 10-5
2022-10-20T19:30:11.170765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31488
 
6.6%
36428
 
5.8%
42332
 
4.5%
32300
 
4.1%
44292
 
3.9%
40292
 
3.9%
39280
 
3.8%
35274
 
3.7%
37260
 
3.5%
47252
 
3.4%
Other values (48)4208
56.8%
ValueCountFrequency (%)
74
 
0.1%
94
 
0.1%
104
 
0.1%
114
 
0.1%
138
0.1%
1412
0.2%
158
0.1%
168
0.1%
1716
0.2%
1812
0.2%
ValueCountFrequency (%)
1224
 
0.1%
1004
 
0.1%
974
 
0.1%
934
 
0.1%
908
 
0.1%
878
 
0.1%
8024
 
0.3%
7724
 
0.3%
7428
 
0.4%
7172
1.0%
2022-10-20T18:51:33.180730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31488
 
6.6%
36428
 
5.8%
42332
 
4.5%
32300
 
4.1%
44292
 
3.9%
40292
 
3.9%
39280
 
3.8%
35274
 
3.7%
37260
 
3.5%
47252
 
3.4%
Other values (48)4208
56.8%
ValueCountFrequency (%)
74
 
0.1%
94
 
0.1%
104
 
0.1%
114
 
0.1%
138
0.1%
1412
0.2%
158
0.1%
168
0.1%
1716
0.2%
1812
0.2%
ValueCountFrequency (%)
1224
 
0.1%
1004
 
0.1%
974
 
0.1%
934
 
0.1%
908
 
0.1%
878
 
0.1%
8024
 
0.3%
7724
 
0.3%
7428
 
0.4%
7172
1.0%
2022-10-20T19:30:11.343266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
Parts per billion
7406 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters125902
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion7406
100.0%

Length

2022-10-20T18:51:33.430341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
Parts per billion
7406 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters125902
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per billion
2nd rowParts per billion
3rd rowParts per billion
4th rowParts per billion
5th rowParts per billion

Common Values

ValueCountFrequency (%)
Parts per billion7406
100.0%

Length

2022-10-20T19:30:11.476024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:51:33.550861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:11.560704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts7406
33.3%
per7406
33.3%
billion7406
33.3%

Most occurring characters

ValueCountFrequency (%)
r14812
11.8%
14812
11.8%
i14812
11.8%
l14812
11.8%
P7406
 
5.9%
a7406
 
5.9%
t7406
 
5.9%
s7406
 
5.9%
p7406
 
5.9%
e7406
 
5.9%
Other values (3)22218
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter103684
82.4%
Space Separator14812
 
11.8%
Uppercase Letter7406
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r14812
14.3%
i14812
14.3%
l14812
14.3%
a7406
7.1%
t7406
7.1%
s7406
7.1%
p7406
7.1%
e7406
7.1%
b7406
7.1%
o7406
7.1%
Space Separator
ValueCountFrequency (%)
14812
100.0%
Uppercase Letter
ValueCountFrequency (%)
P7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin111090
88.2%
Common14812
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r14812
13.3%
i14812
13.3%
l14812
13.3%
P7406
6.7%
a7406
6.7%
t7406
6.7%
s7406
6.7%
p7406
6.7%
e7406
6.7%
b7406
6.7%
Other values (2)14812
13.3%
Common
ValueCountFrequency (%)
14812
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII125902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r14812
11.8%
14812
11.8%
i14812
11.8%
l14812
11.8%
P7406
 
5.9%
a7406
 
5.9%
t7406
 
5.9%
s7406
 
5.9%
p7406
 
5.9%
e7406
 
5.9%
Other values (3)22218
17.6%

SO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct417
Distinct (%)0.0563056980826357
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.20693120658925196
Minimum-0.033333
Maximum1.35
Zeros290
Zeros (%)0.03915743991358358
Memory size59376
2022-10-20T18:51:33.650997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts7406
33.3%
per7406
33.3%
billion7406
33.3%

Most occurring characters

ValueCountFrequency (%)
r14812
11.8%
14812
11.8%
i14812
11.8%
l14812
11.8%
P7406
 
5.9%
a7406
 
5.9%
t7406
 
5.9%
s7406
 
5.9%
p7406
 
5.9%
e7406
 
5.9%
Other values (3)22218
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter103684
82.4%
Space Separator14812
 
11.8%
Uppercase Letter7406
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r14812
14.3%
i14812
14.3%
l14812
14.3%
a7406
7.1%
t7406
7.1%
s7406
7.1%
p7406
7.1%
e7406
7.1%
b7406
7.1%
o7406
7.1%
Space Separator
ValueCountFrequency (%)
14812
100.0%
Uppercase Letter
ValueCountFrequency (%)
P7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin111090
88.2%
Common14812
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r14812
13.3%
i14812
13.3%
l14812
13.3%
P7406
6.7%
a7406
6.7%
t7406
6.7%
s7406
6.7%
p7406
6.7%
e7406
6.7%
b7406
6.7%
Other values (2)14812
13.3%
Common
ValueCountFrequency (%)
14812
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII125902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r14812
11.8%
14812
11.8%
i14812
11.8%
l14812
11.8%
P7406
 
5.9%
a7406
 
5.9%
t7406
 
5.9%
s7406
 
5.9%
p7406
 
5.9%
e7406
 
5.9%
Other values (3)22218
17.6%

SO2 Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct417
Distinct (%)0.0563056980826357
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.20693120658925196
Minimum-0.033333
Maximum1.35
Zeros290
Zeros (%)0.03915743991358358
Memory size59376
2022-10-20T19:30:11.623073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.033333
5-th percentile0.008333
Q10.0875
median0.175
Q30.283333
95-th percentile0.525
Maximum1.35
Range1.383333
Interquartile range (IQR)0.195833

Descriptive statistics

Standard deviation0.1692861335
Coefficient of variation (CV)0.8180792846
Kurtosis3.940679682
Mean0.2069312066
Median Absolute Deviation (MAD)0.1
Skewness1.56813128
Sum1532.532516
Variance0.02865779498
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.343543333 × 10-14
2022-10-20T18:51:33.812674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.033333
5-th percentile0.008333
Q10.0875
median0.175
Q30.283333
95-th percentile0.525
Maximum1.35
Range1.383333
Interquartile range (IQR)0.195833

Descriptive statistics

Standard deviation0.1692861335
Coefficient of variation (CV)0.8180792846
Kurtosis3.940679682
Mean0.2069312066
Median Absolute Deviation (MAD)0.1
Skewness1.56813128
Sum1532.532516
Variance0.02865779498
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.343543333 × 10-14
2022-10-20T19:30:11.721503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0290
 
3.9%
0.1125184
 
2.5%
0.1180
 
2.4%
0.15164
 
2.2%
0.0875160
 
2.2%
0.075158
 
2.1%
0.2152
 
2.1%
0.025148
 
2.0%
0.1875142
 
1.9%
0.2125138
 
1.9%
Other values (407)5690
76.8%
ValueCountFrequency (%)
-0.0333332
 
< 0.1%
-0.0291672
 
< 0.1%
-0.0252
 
< 0.1%
-0.0208332
 
< 0.1%
-0.01256
 
0.1%
-0.0083332
 
< 0.1%
0290
3.9%
0.00416728
 
0.4%
0.0043484
 
0.1%
0.0045456
 
0.1%
ValueCountFrequency (%)
1.352
< 0.1%
1.33752
< 0.1%
1.1217392
< 0.1%
1.1166672
< 0.1%
1.08754
0.1%
1.0714292
< 0.1%
1.06252
< 0.1%
1.0541672
< 0.1%
1.0252
< 0.1%
0.9791672
< 0.1%
2022-10-20T18:51:34.081802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0290
 
3.9%
0.1125184
 
2.5%
0.1180
 
2.4%
0.15164
 
2.2%
0.0875160
 
2.2%
0.075158
 
2.1%
0.2152
 
2.1%
0.025148
 
2.0%
0.1875142
 
1.9%
0.2125138
 
1.9%
Other values (407)5690
76.8%
ValueCountFrequency (%)
-0.0333332
 
< 0.1%
-0.0291672
 
< 0.1%
-0.0252
 
< 0.1%
-0.0208332
 
< 0.1%
-0.01256
 
0.1%
-0.0083332
 
< 0.1%
0290
3.9%
0.00416728
 
0.4%
0.0043484
 
0.1%
0.0045456
 
0.1%
ValueCountFrequency (%)
1.352
< 0.1%
1.33752
< 0.1%
1.1217392
< 0.1%
1.1166672
< 0.1%
1.08754
0.1%
1.0714292
< 0.1%
1.06252
< 0.1%
1.0541672
< 0.1%
1.0252
< 0.1%
0.9791672
< 0.1%
2022-10-20T19:30:11.909748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct69
Distinct (%)0.009316770186335404
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.7514717796381313
Minimum0.0
Maximum12.4
Zeros298
Zeros (%)0.04023764515257899
Memory size59376
2022-10-20T18:51:34.297255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Value
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL
ZEROS

Distinct69
Distinct (%)0.009316770186335404
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.7514717796381313
Minimum0.0
Maximum12.4
Zeros298
Zeros (%)0.04023764515257899
Memory size59376
2022-10-20T19:30:12.043077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.2
median0.5
Q30.9
95-th percentile2.4
Maximum12.4
Range12.4
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.9124969977
Coefficient of variation (CV)1.214279794
Kurtosis20.79444161
Mean0.7514717796
Median Absolute Deviation (MAD)0.3
Skewness3.577330089
Sum5565.4
Variance0.8326507708
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.378720586 × 10-16
2022-10-20T18:51:34.452097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.2
median0.5
Q30.9
95-th percentile2.4
Maximum12.4
Range12.4
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.9124969977
Coefficient of variation (CV)1.214279794
Kurtosis20.79444161
Mean0.7514717796
Median Absolute Deviation (MAD)0.3
Skewness3.577330089
Sum5565.4
Variance0.8326507708
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.378720586 × 10-16
2022-10-20T19:30:12.146227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2910
12.3%
0.3876
11.8%
0.4780
10.5%
0.1776
10.5%
0.5608
 
8.2%
0.6470
 
6.3%
0.7378
 
5.1%
0.9314
 
4.2%
0298
 
4.0%
0.8286
 
3.9%
Other values (59)1710
23.1%
ValueCountFrequency (%)
0298
 
4.0%
0.1776
10.5%
0.2910
12.3%
0.3876
11.8%
0.4780
10.5%
0.5608
8.2%
0.6470
6.3%
0.7378
5.1%
0.8286
 
3.9%
0.9314
 
4.2%
ValueCountFrequency (%)
12.42
< 0.1%
9.62
< 0.1%
7.82
< 0.1%
7.72
< 0.1%
7.52
< 0.1%
7.42
< 0.1%
7.32
< 0.1%
7.22
< 0.1%
6.82
< 0.1%
6.64
0.1%
2022-10-20T18:51:34.734813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2910
12.3%
0.3876
11.8%
0.4780
10.5%
0.1776
10.5%
0.5608
 
8.2%
0.6470
 
6.3%
0.7378
 
5.1%
0.9314
 
4.2%
0298
 
4.0%
0.8286
 
3.9%
Other values (59)1710
23.1%
ValueCountFrequency (%)
0298
 
4.0%
0.1776
10.5%
0.2910
12.3%
0.3876
11.8%
0.4780
10.5%
0.5608
8.2%
0.6470
6.3%
0.7378
5.1%
0.8286
 
3.9%
0.9314
 
4.2%
ValueCountFrequency (%)
12.42
< 0.1%
9.62
< 0.1%
7.82
< 0.1%
7.72
< 0.1%
7.52
< 0.1%
7.42
< 0.1%
7.32
< 0.1%
7.22
< 0.1%
6.82
< 0.1%
6.64
0.1%
2022-10-20T19:30:12.292245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.99621928
Minimum0
Maximum23
Zeros288
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size58.0 KiB
2022-10-20T18:51:34.963290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

SO2 1st Max Hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.99621928
Minimum0
Maximum23
Zeros288
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size58.0 KiB
2022-10-20T19:30:12.419369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median11
Q314
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.113443573
Coefficient of variation (CV)0.5559586815
Kurtosis-0.6109820149
Mean10.99621928
Median Absolute Deviation (MAD)3
Skewness0.2607210299
Sum81438
Variance37.37419232
MonotonicityNot monotonic
2022-10-20T18:51:35.092058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median11
Q314
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.113443573
Coefficient of variation (CV)0.5559586815
Kurtosis-0.6109820149
Mean10.99621928
Median Absolute Deviation (MAD)3
Skewness0.2607210299
Sum81438
Variance37.37419232
MonotonicityNot monotonic
2022-10-20T19:30:12.508975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
111396
18.8%
81294
17.5%
2642
8.7%
20632
8.5%
9478
 
6.5%
10362
 
4.9%
23320
 
4.3%
14318
 
4.3%
0288
 
3.9%
7234
 
3.2%
Other values (14)1442
19.5%
ValueCountFrequency (%)
0288
 
3.9%
172
 
1.0%
2642
8.7%
344
 
0.6%
424
 
0.3%
5126
 
1.7%
680
 
1.1%
7234
 
3.2%
81294
17.5%
9478
 
6.5%
ValueCountFrequency (%)
23320
4.3%
2272
 
1.0%
21160
 
2.2%
20632
8.5%
19190
 
2.6%
18148
 
2.0%
17224
 
3.0%
1638
 
0.5%
1552
 
0.7%
14318
4.3%

SO2 AQI
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct10
Distinct (%)0.3%
Missing3702
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean0.6852051836
Minimum0
Maximum17
Zeros2596
Zeros (%)35.1%
Negative0
Negative (%)0.0%
Memory size58.0 KiB
2022-10-20T18:51:35.212830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
111396
18.8%
81294
17.5%
2642
8.7%
20632
8.5%
9478
 
6.5%
10362
 
4.9%
23320
 
4.3%
14318
 
4.3%
0288
 
3.9%
7234
 
3.2%
Other values (14)1442
19.5%
ValueCountFrequency (%)
0288
 
3.9%
172
 
1.0%
2642
8.7%
344
 
0.6%
424
 
0.3%
5126
 
1.7%
680
 
1.1%
7234
 
3.2%
81294
17.5%
9478
 
6.5%
ValueCountFrequency (%)
23320
4.3%
2272
 
1.0%
21160
 
2.2%
20632
8.5%
19190
 
2.6%
18148
 
2.0%
17224
 
3.0%
1638
 
0.5%
1552
 
0.7%
14318
4.3%

SO2 AQI
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct10
Distinct (%)0.3%
Missing3702
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean0.6852051836
Minimum0
Maximum17
Zeros2596
Zeros (%)35.1%
Negative0
Negative (%)0.0%
Memory size58.0 KiB
2022-10-20T19:30:12.577258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.536920655
Coefficient of variation (CV)2.243007922
Kurtosis18.08592009
Mean0.6852051836
Median Absolute Deviation (MAD)0
Skewness3.614292775
Sum2538
Variance2.3621251
MonotonicityNot monotonic
2022-10-20T18:51:35.413565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.536920655
Coefficient of variation (CV)2.243007922
Kurtosis18.08592009
Mean0.6852051836
Median Absolute Deviation (MAD)0
Skewness3.614292775
Sum2538
Variance2.3621251
MonotonicityNot monotonic
2022-10-20T19:30:12.653474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
02596
35.1%
1666
 
9.0%
3250
 
3.4%
488
 
1.2%
650
 
0.7%
726
 
0.4%
1012
 
0.2%
912
 
0.2%
172
 
< 0.1%
132
 
< 0.1%
(Missing)3702
50.0%
ValueCountFrequency (%)
02596
35.1%
1666
 
9.0%
3250
 
3.4%
488
 
1.2%
650
 
0.7%
726
 
0.4%
912
 
0.2%
1012
 
0.2%
132
 
< 0.1%
172
 
< 0.1%
ValueCountFrequency (%)
172
 
< 0.1%
132
 
< 0.1%
1012
 
0.2%
912
 
0.2%
726
 
0.4%
650
 
0.7%
488
 
1.2%
3250
 
3.4%
1666
 
9.0%
02596
35.1%

CO Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
Parts per million
7406 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters125902
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million7406
100.0%

Length

2022-10-20T18:51:35.583017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
02596
35.1%
1666
 
9.0%
3250
 
3.4%
488
 
1.2%
650
 
0.7%
726
 
0.4%
1012
 
0.2%
912
 
0.2%
172
 
< 0.1%
132
 
< 0.1%
(Missing)3702
50.0%
ValueCountFrequency (%)
02596
35.1%
1666
 
9.0%
3250
 
3.4%
488
 
1.2%
650
 
0.7%
726
 
0.4%
912
 
0.2%
1012
 
0.2%
132
 
< 0.1%
172
 
< 0.1%
ValueCountFrequency (%)
172
 
< 0.1%
132
 
< 0.1%
1012
 
0.2%
912
 
0.2%
726
 
0.4%
650
 
0.7%
488
 
1.2%
3250
 
3.4%
1666
 
9.0%
02596
35.1%

CO Units
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
Parts per million
7406 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters125902
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParts per million
2nd rowParts per million
3rd rowParts per million
4th rowParts per million
5th rowParts per million

Common Values

ValueCountFrequency (%)
Parts per million7406
100.0%

Length

2022-10-20T19:30:12.735531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T18:51:35.749888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-20T19:30:12.810641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts7406
33.3%
per7406
33.3%
million7406
33.3%

Most occurring characters

ValueCountFrequency (%)
r14812
11.8%
14812
11.8%
i14812
11.8%
l14812
11.8%
P7406
 
5.9%
a7406
 
5.9%
t7406
 
5.9%
s7406
 
5.9%
p7406
 
5.9%
e7406
 
5.9%
Other values (3)22218
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter103684
82.4%
Space Separator14812
 
11.8%
Uppercase Letter7406
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r14812
14.3%
i14812
14.3%
l14812
14.3%
a7406
7.1%
t7406
7.1%
s7406
7.1%
p7406
7.1%
e7406
7.1%
m7406
7.1%
o7406
7.1%
Space Separator
ValueCountFrequency (%)
14812
100.0%
Uppercase Letter
ValueCountFrequency (%)
P7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin111090
88.2%
Common14812
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r14812
13.3%
i14812
13.3%
l14812
13.3%
P7406
6.7%
a7406
6.7%
t7406
6.7%
s7406
6.7%
p7406
6.7%
e7406
6.7%
m7406
6.7%
Other values (2)14812
13.3%
Common
ValueCountFrequency (%)
14812
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII125902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r14812
11.8%
14812
11.8%
i14812
11.8%
l14812
11.8%
P7406
 
5.9%
a7406
 
5.9%
t7406
 
5.9%
s7406
 
5.9%
p7406
 
5.9%
e7406
 
5.9%
Other values (3)22218
17.6%

CO Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct736
Distinct (%)0.09937888198757763
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.20885727855792602
Minimum0.045833
Maximum0.554792
Zeros0
Zeros (%)0.0
Memory size59376
2022-10-20T18:51:35.877217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
parts7406
33.3%
per7406
33.3%
million7406
33.3%

Most occurring characters

ValueCountFrequency (%)
r14812
11.8%
14812
11.8%
i14812
11.8%
l14812
11.8%
P7406
 
5.9%
a7406
 
5.9%
t7406
 
5.9%
s7406
 
5.9%
p7406
 
5.9%
e7406
 
5.9%
Other values (3)22218
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter103684
82.4%
Space Separator14812
 
11.8%
Uppercase Letter7406
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r14812
14.3%
i14812
14.3%
l14812
14.3%
a7406
7.1%
t7406
7.1%
s7406
7.1%
p7406
7.1%
e7406
7.1%
m7406
7.1%
o7406
7.1%
Space Separator
ValueCountFrequency (%)
14812
100.0%
Uppercase Letter
ValueCountFrequency (%)
P7406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin111090
88.2%
Common14812
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r14812
13.3%
i14812
13.3%
l14812
13.3%
P7406
6.7%
a7406
6.7%
t7406
6.7%
s7406
6.7%
p7406
6.7%
e7406
6.7%
m7406
6.7%
Other values (2)14812
13.3%
Common
ValueCountFrequency (%)
14812
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII125902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r14812
11.8%
14812
11.8%
i14812
11.8%
l14812
11.8%
P7406
 
5.9%
a7406
 
5.9%
t7406
 
5.9%
s7406
 
5.9%
p7406
 
5.9%
e7406
 
5.9%
Other values (3)22218
17.6%

CO Mean
Numeric time series

HIGH CORRELATION
NON STATIONARY
SEASONAL

Distinct736
Distinct (%)0.09937888198757763
Missing0
Missing (%)0.0
Infinite0
Infinite (%)0.0
Mean0.20885727855792602
Minimum0.045833
Maximum0.554792
Zeros0
Zeros (%)0.0
Memory size59376
2022-10-20T19:30:12.870378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.045833
5-th percentile0.1
Q10.1375
median0.1875
Q30.270833
95-th percentile0.382708
Maximum0.554792
Range0.508959
Interquartile range (IQR)0.133333

Descriptive statistics

Standard deviation0.0894482032
Coefficient of variation (CV)0.4282742925
Kurtosis-0.2097677262
Mean0.2088572786
Median Absolute Deviation (MAD)0.0625
Skewness0.7317067169
Sum1546.797005
Variance0.008000981057
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.005106551243
2022-10-20T18:51:36.049316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.045833
5-th percentile0.1
Q10.1375
median0.1875
Q30.270833
95-th percentile0.382708
Maximum0.554792
Range0.508959
Interquartile range (IQR)0.133333

Descriptive statistics

Standard deviation0.0894482032
Coefficient of variation (CV)0.4282742925
Kurtosis-0.2097677262
Mean0.2088572786
Median Absolute Deviation (MAD)0.0625
Skewness0.7317067169
Sum1546.797005
Variance0.008000981057
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.005106551243
2022-10-20T19:30:12.958525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1480
 
6.5%
0.2196
 
2.6%
0.133333152
 
2.1%
0.129167144
 
1.9%
0.175142
 
1.9%
0.158333138
 
1.9%
0.1375138
 
1.9%
0.1625136
 
1.8%
0.108333132
 
1.8%
0.120833130
 
1.8%
Other values (726)5618
75.9%
ValueCountFrequency (%)
0.0458334
 
0.1%
0.0541678
0.1%
0.0583338
0.1%
0.06258
0.1%
0.0666678
0.1%
0.07083318
0.2%
0.07516
0.2%
0.07916714
0.2%
0.08333318
0.2%
0.0869572
 
< 0.1%
ValueCountFrequency (%)
0.5547922
< 0.1%
0.5044172
< 0.1%
0.5041672
< 0.1%
0.48752
< 0.1%
0.4791672
< 0.1%
0.4778332
< 0.1%
0.4752
< 0.1%
0.4733752
< 0.1%
0.4708752
< 0.1%
0.4708334
0.1%
2022-10-20T18:51:36.394761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1480
 
6.5%
0.2196
 
2.6%
0.133333152
 
2.1%
0.129167144
 
1.9%
0.175142
 
1.9%
0.158333138
 
1.9%
0.1375138
 
1.9%
0.1625136
 
1.8%
0.108333132
 
1.8%
0.120833130
 
1.8%
Other values (726)5618
75.9%
ValueCountFrequency (%)
0.0458334
 
0.1%
0.0541678
0.1%
0.0583338
0.1%
0.06258
0.1%
0.0666678
0.1%
0.07083318
0.2%
0.07516
0.2%
0.07916714
0.2%
0.08333318
0.2%
0.0869572
 
< 0.1%
ValueCountFrequency (%)
0.5547922
< 0.1%
0.5044172
< 0.1%
0.5041672
< 0.1%
0.48752
< 0.1%
0.4791672
< 0.1%
0.4778332
< 0.1%
0.4752
< 0.1%
0.4733752
< 0.1%
0.4708752
< 0.1%
0.4708334
0.1%
2022-10-20T19:30:13.150162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Value
Real number (ℝ≥0)

HIGH CORRELATION

Distinct380
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3530257899
Minimum0.1
Maximum1.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.0 KiB
2022-10-20T18:51:36.647392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

CO 1st Max Value
Real number (ℝ≥0)

HIGH CORRELATION

Distinct380
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3530257899
Minimum0.1
Maximum1.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.0 KiB
2022-10-20T19:30:13.302054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.2
median0.3
Q30.4975
95-th percentile0.7
Maximum1.2
Range1.1
Interquartile range (IQR)0.2975

Descriptive statistics

Standard deviation0.1785232899
Coefficient of variation (CV)0.5056947538
Kurtosis0.6349354432
Mean0.3530257899
Median Absolute Deviation (MAD)0.1
Skewness0.9172225371
Sum2614.509
Variance0.03187056503
MonotonicityNot monotonic
2022-10-20T18:51:36.838636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.2
median0.3
Q30.4975
95-th percentile0.7
Maximum1.2
Range1.1
Interquartile range (IQR)0.2975

Descriptive statistics

Standard deviation0.1785232899
Coefficient of variation (CV)0.5056947538
Kurtosis0.6349354432
Mean0.3530257899
Median Absolute Deviation (MAD)0.1
Skewness0.9172225371
Sum2614.509
Variance0.03187056503
MonotonicityNot monotonic
2022-10-20T19:30:13.403618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.21818
24.5%
0.31496
20.2%
0.41025
13.8%
0.5702
 
9.5%
0.1550
 
7.4%
0.6392
 
5.3%
0.7178
 
2.4%
0.8116
 
1.6%
0.940
 
0.5%
0.33210
 
0.1%
Other values (370)1079
14.6%
ValueCountFrequency (%)
0.1550
7.4%
0.1392
 
< 0.1%
0.1542
 
< 0.1%
0.162
 
< 0.1%
0.1662
 
< 0.1%
0.1672
 
< 0.1%
0.172
 
< 0.1%
0.1714
 
0.1%
0.1722
 
< 0.1%
0.1732
 
< 0.1%
ValueCountFrequency (%)
1.22
 
< 0.1%
1.12
 
< 0.1%
1.0722
 
< 0.1%
1.0282
 
< 0.1%
1.0172
 
< 0.1%
18
0.1%
0.9862
 
< 0.1%
0.9652
 
< 0.1%
0.9592
 
< 0.1%
0.9514
0.1%

CO 1st Max Hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.113016473
Minimum0
Maximum23
Zeros2491
Zeros (%)33.6%
Negative0
Negative (%)0.0%
Memory size58.0 KiB
2022-10-20T18:51:37.024329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.21818
24.5%
0.31496
20.2%
0.41025
13.8%
0.5702
 
9.5%
0.1550
 
7.4%
0.6392
 
5.3%
0.7178
 
2.4%
0.8116
 
1.6%
0.940
 
0.5%
0.33210
 
0.1%
Other values (370)1079
14.6%
ValueCountFrequency (%)
0.1550
7.4%
0.1392
 
< 0.1%
0.1542
 
< 0.1%
0.162
 
< 0.1%
0.1662
 
< 0.1%
0.1672
 
< 0.1%
0.172
 
< 0.1%
0.1714
 
0.1%
0.1722
 
< 0.1%
0.1732
 
< 0.1%
ValueCountFrequency (%)
1.22
 
< 0.1%
1.12
 
< 0.1%
1.0722
 
< 0.1%
1.0282
 
< 0.1%
1.0172
 
< 0.1%
18
0.1%
0.9862
 
< 0.1%
0.9652
 
< 0.1%
0.9592
 
< 0.1%
0.9514
0.1%

CO 1st Max Hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.113016473
Minimum0
Maximum23
Zeros2491
Zeros (%)33.6%
Negative0
Negative (%)0.0%
Memory size58.0 KiB
2022-10-20T19:30:13.483772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q39
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.775985863
Coefficient of variation (CV)1.093205097
Kurtosis-0.4774269531
Mean7.113016473
Median Absolute Deviation (MAD)6
Skewness0.921960795
Sum52679
Variance60.46595614
MonotonicityNot monotonic
2022-10-20T18:51:37.183559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q39
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.775985863
Coefficient of variation (CV)1.093205097
Kurtosis-0.4774269531
Mean7.113016473
Median Absolute Deviation (MAD)6
Skewness0.921960795
Sum52679
Variance60.46595614
MonotonicityNot monotonic
2022-10-20T19:30:13.559050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
02491
33.6%
7873
 
11.8%
8738
 
10.0%
21416
 
5.6%
1382
 
5.2%
6382
 
5.2%
22382
 
5.2%
9326
 
4.4%
23294
 
4.0%
2202
 
2.7%
Other values (14)920
 
12.4%
ValueCountFrequency (%)
02491
33.6%
1382
 
5.2%
2202
 
2.7%
3128
 
1.7%
470
 
0.9%
5140
 
1.9%
6382
 
5.2%
7873
 
11.8%
8738
 
10.0%
9326
 
4.4%
ValueCountFrequency (%)
23294
4.0%
22382
5.2%
21416
5.6%
20182
2.5%
19106
 
1.4%
1854
 
0.7%
1722
 
0.3%
164
 
0.1%
156
 
0.1%
146
 
0.1%

CO AQI
Numeric time series

HIGH CORRELATION
MISSING
NON STATIONARY
SEASONAL

Distinct8
Distinct (%)0.002160410477990818
Missing3703
Missing (%)0.5
Infinite0
Infinite (%)0.0
Mean3.2749122333243315
Minimum1.0
Maximum9.0
Zeros0
Zeros (%)0.0
Memory size59376
2022-10-20T18:51:37.379106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
02491
33.6%
7873
 
11.8%
8738
 
10.0%
21416
 
5.6%
1382
 
5.2%
6382
 
5.2%
22382
 
5.2%
9326
 
4.4%
23294
 
4.0%
2202
 
2.7%
Other values (14)920
 
12.4%
ValueCountFrequency (%)
02491
33.6%
1382
 
5.2%
2202
 
2.7%
3128
 
1.7%
470
 
0.9%
5140
 
1.9%
6382
 
5.2%
7873
 
11.8%
8738
 
10.0%
9326
 
4.4%
ValueCountFrequency (%)
23294
4.0%
22382
5.2%
21416
5.6%
20182
2.5%
19106
 
1.4%
1854
 
0.7%
1722
 
0.3%
164
 
0.1%
156
 
0.1%
146
 
0.1%

CO AQI
Numeric time series

HIGH CORRELATION
MISSING
NON STATIONARY
SEASONAL

Distinct8
Distinct (%)0.002160410477990818
Missing3703
Missing (%)0.5
Infinite0
Infinite (%)0.0
Mean3.2749122333243315
Minimum1.0
Maximum9.0
Zeros0
Zeros (%)0.0
Memory size59376
2022-10-20T19:30:13.631024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.771993794
Coefficient of variation (CV)0.5410813078
Kurtosis-0.5899090217
Mean3.274912233
Median Absolute Deviation (MAD)1
Skewness0.6954079311
Sum12127
Variance3.139962006
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.01398145428
2022-10-20T18:51:37.523151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.771993794
Coefficient of variation (CV)0.5410813078
Kurtosis-0.5899090217
Mean3.274912233
Median Absolute Deviation (MAD)1
Skewness0.6954079311
Sum12127
Variance3.139962006
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.01398145428
2022-10-20T19:30:13.704150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
21224
 
16.5%
3880
 
11.9%
5617
 
8.3%
1428
 
5.8%
6392
 
5.3%
7132
 
1.8%
820
 
0.3%
910
 
0.1%
(Missing)3703
50.0%
ValueCountFrequency (%)
1428
 
5.8%
21224
16.5%
3880
11.9%
5617
8.3%
6392
 
5.3%
7132
 
1.8%
820
 
0.3%
910
 
0.1%
ValueCountFrequency (%)
910
 
0.1%
820
 
0.3%
7132
 
1.8%
6392
 
5.3%
5617
8.3%
3880
11.9%
21224
16.5%
1428
 
5.8%
2022-10-20T18:51:37.861734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
21224
 
16.5%
3880
 
11.9%
5617
 
8.3%
1428
 
5.8%
6392
 
5.3%
7132
 
1.8%
820
 
0.3%
910
 
0.1%
(Missing)3703
50.0%
ValueCountFrequency (%)
1428
 
5.8%
21224
16.5%
3880
11.9%
5617
8.3%
6392
 
5.3%
7132
 
1.8%
820
 
0.3%
910
 
0.1%
ValueCountFrequency (%)
910
 
0.1%
820
 
0.3%
7132
 
1.8%
6392
 
5.3%
5617
8.3%
3880
11.9%
21224
16.5%
1428
 
5.8%
2022-10-20T19:30:13.830369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

Interactions

2022-10-20T18:51:23.080791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ACF and PACF

Interactions

2022-10-20T19:30:04.768905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:51:21.363740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:30:03.619827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:51:21.848942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:30:03.906099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:51:22.602113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:30:04.454608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:51:23.204523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:30:04.834771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:51:21.486914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:30:03.691239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:51:21.970229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:30:04.212091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:51:22.718241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:30:04.525115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:51:23.327463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:30:04.910663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:51:21.614078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:30:03.766167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:51:22.359687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:30:04.305717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:51:22.844686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:30:04.623606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:51:23.459156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:30:04.979675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:51:21.728702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:30:03.835509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:51:22.482337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:30:04.384380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T18:51:22.968678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-20T19:30:04.700531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-20T18:51:38.100573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/2022-10-20T19:30:13.962313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-10-20T18:51:38.323430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-10-20T19:30:14.112958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-20T18:51:38.588124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-20T19:30:14.262804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-20T18:51:38.833180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-20T19:30:14.409071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-20T18:51:39.060563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-20T19:30:14.560278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-20T18:51:39.228098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-20T19:30:14.679470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-20T18:51:23.746423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-20T19:30:05.247831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-20T18:51:24.286854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-20T19:30:05.579403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-20T18:51:24.532205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-20T19:30:05.741190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-20T18:51:24.661256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-20T19:30:05.824876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
04191028400 W RIVER ROADArizonaPimaTucson2010-10-01Parts per billion10.39166718.52017Parts per million0.0272500.0461039Parts per billion0.1708330.390.0Parts per million0.1583330.38NaN
14191028400 W RIVER ROADArizonaPimaTucson2010-10-01Parts per billion10.39166718.52017Parts per million0.0272500.0461039Parts per billion0.1708330.390.0Parts per million0.1791670.303.0
24191028400 W RIVER ROADArizonaPimaTucson2010-10-01Parts per billion10.39166718.52017Parts per million0.0272500.0461039Parts per billion0.1375000.211NaNParts per million0.1583330.38NaN
34191028400 W RIVER ROADArizonaPimaTucson2010-10-01Parts per billion10.39166718.52017Parts per million0.0272500.0461039Parts per billion0.1375000.211NaNParts per million0.1791670.303.0
44191028400 W RIVER ROADArizonaPimaTucson2010-10-02Parts per billion6.18333312.2311Parts per million0.0300420.0481041Parts per billion0.1500000.4120.0Parts per million0.1208330.21NaN
54191028400 W RIVER ROADArizonaPimaTucson2010-10-02Parts per billion6.18333312.2311Parts per million0.0300420.0481041Parts per billion0.1500000.4120.0Parts per million0.1375000.212.0
64191028400 W RIVER ROADArizonaPimaTucson2010-10-02Parts per billion6.18333312.2311Parts per million0.0300420.0481041Parts per billion0.1000000.314NaNParts per million0.1208330.21NaN
74191028400 W RIVER ROADArizonaPimaTucson2010-10-02Parts per billion6.18333312.2311Parts per million0.0300420.0481041Parts per billion0.1000000.314NaNParts per million0.1375000.212.0
84191028400 W RIVER ROADArizonaPimaTucson2010-10-03Parts per billion5.57083311.5010Parts per million0.0312080.0451138Parts per billion0.0500000.100.0Parts per million0.1083330.26NaN
94191028400 W RIVER ROADArizonaPimaTucson2010-10-03Parts per billion5.57083311.5010Parts per million0.0312080.0451138Parts per billion0.0500000.100.0Parts per million0.1000000.101.0

Last rows

State CodeCounty CodeSite NumAddressStateCountyCityDate LocalNO2 UnitsNO2 MeanNO2 1st Max ValueNO2 1st Max HourNO2 AQIO3 UnitsO3 MeanO3 1st Max ValueO3 1st Max HourO3 AQISO2 UnitsSO2 MeanSO2 1st Max ValueSO2 1st Max HourSO2 AQICO UnitsCO MeanCO 1st Max ValueCO 1st Max HourCO AQI
73964191028400 W RIVER ROADArizonaPimaTucson2016-03-29Parts per billion1.78755.865Parts per million0.0414170.0451042Parts per billion0.0000000.000.0Parts per million0.1888330.2156NaN
73974191028400 W RIVER ROADArizonaPimaTucson2016-03-29Parts per billion1.78755.865Parts per million0.0414170.0451042Parts per billion0.0000000.000.0Parts per million0.2000000.20002.0
73984191028400 W RIVER ROADArizonaPimaTucson2016-03-30Parts per billion2.462510.8229Parts per million0.0385000.046943Parts per billion0.0000000.02NaNParts per million0.2048330.26622NaN
73994191028400 W RIVER ROADArizonaPimaTucson2016-03-30Parts per billion2.462510.8229Parts per million0.0385000.046943Parts per billion0.0000000.02NaNParts per million0.2000000.20002.0
74004191028400 W RIVER ROADArizonaPimaTucson2016-03-30Parts per billion2.462510.8229Parts per million0.0385000.046943Parts per billion0.0083330.1210.0Parts per million0.2048330.26622NaN
74014191028400 W RIVER ROADArizonaPimaTucson2016-03-30Parts per billion2.462510.8229Parts per million0.0385000.046943Parts per billion0.0083330.1210.0Parts per million0.2000000.20002.0
74024191028400 W RIVER ROADArizonaPimaTucson2016-03-31Parts per billion9.250027.42125Parts per million0.0334210.0501146Parts per billion0.0541670.270.0Parts per million0.2541670.30053.0
74034191028400 W RIVER ROADArizonaPimaTucson2016-03-31Parts per billion9.250027.42125Parts per million0.0334210.0501146Parts per billion0.0250000.18NaNParts per million0.2774580.49222NaN
74044191028400 W RIVER ROADArizonaPimaTucson2016-03-31Parts per billion9.250027.42125Parts per million0.0334210.0501146Parts per billion0.0541670.270.0Parts per million0.2774580.49222NaN
74054191028400 W RIVER ROADArizonaPimaTucson2016-03-31Parts per billion9.250027.42125Parts per million0.0334210.0501146Parts per billion0.0250000.18NaNParts per million0.2541670.30053.0